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Record W4280506519 · doi:10.1002/jev2.12224

ISEV2022 Abstract Book

2022· article· de· W4280506519 on OpenAlexaff

Bibliographic record

VenueJournal of Extracellular Vesicles · 2022
Typearticle
Languagede
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsSickKids FoundationFonds de Recherche du Québec - SantéHospital for Sick ChildrenMontreal Children's HospitalMcGill UniversityDouglas Mental Health University InstituteDouglas College
FundersEuropean Social FundCore Research for Evolutional Science and TechnologyNational Institute of Neurological Disorders and StrokeNational Cancer InstituteNational Human Genome Research InstituteNational Institute on Drug AbuseUniversitätsklinikum Hamburg-EppendorfEuropean Regional Development FundFundação para a Ciência e a TecnologiaNational Medical Research CouncilNational Health and Medical Research CouncilStanford Bio-XNational Institutes of HealthAgencia Nacional de Investigación y DesarrolloSzegedi TudományegyetemBusiness FinlandFondation CharcotBritish Heart FoundationMinistry of Education, Culture, Sports, Science and TechnologyChina Scholarship CouncilMinistero dell’Istruzione, dell’Università e della RicercaFonds De La Recherche Scientifique - FNRSConselho Nacional de Desenvolvimento Científico e TecnológicoGedeon RichterMagyar Tudományos AkadémiaInstitut National de la Santé et de la Recherche MédicaleMinistero della SaluteChina Postdoctoral Science FoundationGeneralitat ValencianaSlovak Academic Information AgencyScience Foundation IrelandKorea Health Industry Development InstituteDiabetes AustraliaEngineering and Physical Sciences Research CouncilEuropean CommissionFundação de Amparo à Pesquisa do Estado de São PauloDiabetes UKDeutsche ForschungsgemeinschaftNederlandse Organisatie voor Wetenschappelijk OnderzoekFondazione Regionale per la Ricerca BiomedicaMinistry of Trade, Industry and EnergyInternational Society on Thrombosis and HaemostasisUniversity of Central FloridaComisión Nacional de Investigación Científica y TecnológicaMedical Research CouncilLions Medical Research FoundationJoachim Herz StiftungWellcome TrustMinistry of Science and ICT, South KoreaHealth Service ExecutiveInstituto de Salud Carlos IIINational Research Foundation of KoreaAgencia Estatal de InvestigaciónAssociation Nationale de la Recherche et de la TechnologieLeona M. and Harry B. Helmsley Charitable TrustNational Heart, Lung, and Blood InstituteNanyang Technological UniversityRheinische Friedrich-Wilhelms-Universität BonnRussian Science FoundationEuroNanoMed IIIMinisterio de Ciencia e InnovaciónUniversität HamburgSanofiAgència de Gestió d'Ajuts Universitaris i de RecercaNational Research FoundationAgence Nationale de la RechercheFundación para el Fomento en Asturias de la Investigación Científica Aplicada y la TecnologíaNarodowym Centrum NaukiNational Institute of General Medical SciencesNierstichtingWilliam K. Warren Foundation
KeywordsComputer scienceComputational biologyBiology

Abstract

fetched live from OpenAlex

About ISEVThe International Society for Extracellular Vesicles is the leading professional society for extracellular vesicle research.ISEV's mission is advancing extracellular vesicle research globally.Our vision is to be the leading advocate and guide of extracellular vesicle research and to advance the understanding of extracellular vesicle biology. ISEV Annual MeetingThe ISEV annual meeting is the premier international conference of extracellular vesicle research, covering the latest in exosomes, microvesicles and more.With an anticipated 1,000+ attendees, ISEV2022 will feature presentations from the top researchers in the field, as well as providing opportunities for talks from students and early career researchers.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.067
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0040.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.9330.897

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.242
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2022
Admission routes1
Has abstractyes

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